Preface Index of Applications PART I. EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here! 1.1 What Is Statistics? 1.2 Data 1.3 Variables 2. Displaying and Describing Categorical Data 2.1 Summarizing and Displaying a Single Categorical Variable 2.
2 Exploring the Relationship Between Two Categorical Variables 3. Displaying and Summarizing Quantitative Data 3.1 Displaying Quantitative Variables 3.2 Shape 3.3 Center 3.4 Spread 3.5 Boxplots and 5-Number Summaries 3.6 The Center of Symmetric Distributions: The Mean 3.
7 The Spread of Symmetric Distributions: The Standard Deviation 3.8 Summary--What to Tell About a Quantitative Variable 4. Understanding and Comparing Distributions 4.1 Comparing Groups with Histograms 4.2 Comparing Groups with Boxplots 4.3 Outliers 4.4 Timeplots: Order, Please! 4.5 Re-expressing Data: A First Look 5.
The Standard Deviation as a Ruler and the Normal Model 5.1 Standardizing with z -Scores 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Finding Normal Percentiles 5.5 Normal Probability Plots Review of Part I. Exploring and Understanding Data PART II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6.
Scatterplots, Association, and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation Causation 6.4 Straightening Scatterplots 7. Linear Regression 7.1 Least Squares: The Line of "Best Fit" 7.2 The Linear Model 7.
3 Finding the Least Squares Line 7.4 Regression to the Mean 7.5 Examining the Residuals 7.6 R2--The Variation Accounted for by the Model 7.7 Regression Assumptions and Conditions 8. Regression Wisdom 8.1 Examining Residuals 8.2 Extrapolation: Reaching Beyond the Data 8.
3 Outliers, Leverage, and Influence 8.4 Lurking Variables and Causation 8.5 Working with Summary Values Review of Part II. Exploring Relationships Between Variables PART III. GATHERING DATA 9. Understanding Randomness 9.1 What is Randomness? 9.2 Simulating By Hand 10.
Sample Surveys 10.1 The Three Big Ideas of Sampling 10.2 Populations and Parameters 10.3 Simple Random Samples 10.4 Other Sampling Designs 10.5 From the Population to the Sample: You Can''t Always Get What You Want 10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly 11.
Experiments and Observational Studies 11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Design 11.4 Control Treatments 11.5 Blocking 11.6 Confounding Review of Part III Gathering Data PART IV. RANDOMNESS AND PROBABILITY 12.
From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability 13. Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.
4 Picturing Probability: Tables, Venn Diagrams and Trees 13.5 Reversing the Conditioning and Bayes'' Rule 14. Random Variables and Probability Models 14.1 Expected Value: Center 14.2 Standard Deviation 14.3 Combining Random Variables 14.4 The Binomial Model 14.5 Modeling the Binomial with a Normal Model *14.
6 The Poisson Model 14.7 Continuous Random Variables Review of Part IV Randomness and Probability PART V. FROM THE DATA AT HAND TO THE WORLD AT LARGE 15. Sampling Distribution Models 15.1 Sampling Distribution of a Proportion 15.2 When Does the Normal Model Work? Assumptions and Conditions 15.3 The Sampling Distribution of Other Statistics 15.4 The Central Limit Theorem: The Fundamental Theorem of Statistics 15.
5 Sampling Distributions: A Summary 16. Confidence Intervals for Proportions 16.1 A Confidence Interval 16.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.3 Margin of Error: Certainty vs. Precision 16.4 Assumptions and Conditions 17. Testing Hypotheses About Proportions 17.
1 Hypotheses 17.2 P -Values 17.3 The Reasoning of Hypothesis Testing 17.4 Alternative Alternatives 17.5 P -Values and Decisions: What to Tell About a Hypothesis Test 18. Inferences About Means 18.1: Getting Started: The Central Limit Theorem (Again) 18.2: Gosset''s t 18.
3 Interpreting Confidence Intervals 18.4 A Hypothesis Test for the Mean 18.5 Choosing the Sample Size 19. More About Tests and Intervals 19.1 Choosing Hypotheses 19.2 How to Think About P Values 19.3 Alpha Levels 19.4 Practical vs.
Statistical Significance 19.5 Critical Values Again 19.6 Errors 19.7 Power Review of Part V From the Data at Hand to the World at Large PART VI. LEARNING ABOUT THE WORLD 20. Comparing Groups 20.1 The Variance of a Difference 20.2 The Standard Deviation of the Difference Between Two Proportions 20.
3 Assumptions and Conditions for Comparing Proportions 20.4 The Sampling Distribution of the Difference between Two Proportions 20.5 Comparing Two Means 20.6 The Two-Sample t-Test: Testing for the Difference Between Two Means 20.7 The Two Sample z-Test: Testing for the Difference between Proportions 20.8 The Pooled t-Test: Everyone into the Pool? 20.9 Pooling 21. Paired Samples and Blocks 21.
1 Paired Data 21.2 Assumptions and Conditions 21.3 Confidence Intervals for Matched Pairs 21.4 Blocking 22. Comparing Counts 22.1 Goodness-of-Fit Tests 22.2 Chi-Square Test of Homogeneity 22.3 Examining the Residuals 22.
4 Chi-Square Test of Independence 23. Inferences for Regression 23.1 The Population and the Sample 23.2 Assumptions and Conditions 23.3 Intuition About Regression Inference 23.4 Regression Inference 23.5 Standard Errors for Predicted Values 23.6 Confidence Intervals for Predicted Values 23.
7* Logistic Regression Review of Part VI. Learning About the World PART VII. INFERENCE WHEN VARIABLES ARE RELATED 24. Analysis of Variance 24.1 Testing Whether the Means of Several Groups Are Equal 24.2 The ANOVA Table 24.3 Plot the Data . 24.
4 Comparing Means 25. Multiple Regression 25.1 Two Predictors 25.2 What Multiple Regression Coefficients Mean 25.3 The Multiple Regression Model 25.4 Multiple Regression Inference 25.5 Comparing Multiple Regression Models Appendices A. Answers B.
Photo Acknowledgments C. Index D. Tables and Selected Formulas *Indicates an optional chapter.